## Nombre de participants à l'expérimentation :  58
## Nombre de participants se déclarant comme joueurs :  29
## Nombre de femmes se déclarant comme joueuses :  3
## Age médian des joueurs :  15

Removing Outliers based on BET

## [1] "Outliers BET STANDARD DEVIATION: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers BET SAVED SHEEPS: "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers BET EXPLOIT DDA: vuq3c2tk6"
## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers:  5"
## [1] "Total number of outliers motor task:  2"
## [1] "Total number of outliers perceptive task:  1"
## [1] "Total number of outliers logical task:  2"

Removing Outliers based on CONFIDENCE SCALE

non nécessaire sur ce fichier !!

{r removing.outliers.setup.cs, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SETUP # #------------------------------------------------------ # # DTM <- DTAll[which(DTAll$nom_du_jeu=="Motrice"),] # DTL <- DTAll[which(DTAll$nom_du_jeu=="Logique2"),] # DTS <- DTAll[which(DTAll$nom_du_jeu=="Sensoriel"),] # # # get.outliers <- function(DTDescMLoc,DTDescSLoc,DTDescLLoc){ # outliersM <- boxplot.stats(DTDescMLoc$var)$out # outliersS <- boxplot.stats(DTDescSLoc$var)$out # outliersL <- boxplot.stats(DTDescLLoc$var)$out # # outliers = data.table(type=character(0),id=character(0)) # setkey(outliers,id) # if(length(outliersM) > 0) # outliers = merge(outliers,data.table(id=DTDescMLoc[var %in% outliersM]$IDjoueur,type="Moteur"),by=c("id","type"),all=TRUE) # if(length(outliersS) > 0) # outliers = merge(outliers,data.table(id=DTDescSLoc[var %in% outliersS]$IDjoueur,type="Sensoriel"),by=c("id","type"),all=TRUE) # if(length(outliersL) > 0) # outliers = merge(outliers,data.table(id=DTDescLLoc[var %in% outliersL]$IDjoueur,type="Logique"),by=c("id","type"),all=TRUE) # # return(outliers) # } # # plot.outliers <- function(DT,title){ # p <- ggplot(DT, # aes(type,var)) + # xlab("Difficulty Type") + # ylab(title) # p <- p + geom_boxplot() + geom_point(shape=1) # print(p) # } #

{r detect.outliers.cs.sd, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS CS STD DEV # #------------------------------------------------------ # DTDescM = DTM[,.(type="Moteur",var=sd(confianceNorm)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=sd(confianceNorm)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=sd(confianceNorm)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "CS Standard Dev"); # # outliers = get.outliers(DTDescM,DTDescS,DTDescL) # print(paste("Outliers CS STANDARD DEVIATION:",toString(outliers$id))) # # DTM[IDjoueur %in% unlist(outliers[type=="Moteur"]$id) ,{plot.diff.curve.cs(.SD,"Outlier CS Sd Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id) ,{plot.diff.curve.cs(.SD,"Outlier CS Sd Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliers[type=="Logique"]$id) ,{plot.diff.curve.cs(.SD,"Outlier CS Sd Logical Task");NULL},by=.(IDjoueur)] #

{r detect.outliers.win.sum.cs, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUM OF WINS # #------------------------------------------------------ # # Difficulty : win sum # # # DTDescM = DTM[,.(type="Moteur",var=sum(gagnant)),by=IDjoueur] # # DTDescS = DTS[,.(type="Sensoriel",var=sum(gagnant)),by=IDjoueur] # # DTDescL = DTL[,.(type="Logique",var=sum(gagnant)),by=IDjoueur] # # # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win Sum"); # # # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # # print(paste("Outliers :",toString(outliersLoc$id))) # # # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Motor Task");NULL},by=.(IDjoueur)] # # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Sensory Task");NULL},by=.(IDjoueur)] # # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Logical Task");NULL},by=.(IDjoueur)] # #

{r detect.outliers.sheeps.saved.cs, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SAVED SHEEPS # #------------------------------------------------------ # # Difficulty and strategy = saved sheeps # # DTDescM = DTM[,.(type="Moteur",var=max(moutons_sauves)),by=IDjoueur] # # DTDescS = DTS[,.(type="Sensoriel",var=max(moutons_sauves)),by=IDjoueur] # # DTDescL = DTL[,.(type="Logique",var=max(moutons_sauves)),by=IDjoueur] # # # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Saved sheeps"); # # # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # # print(paste("Outliers CS SAVED SHEEPS:",toString(outliersLoc$id))) # # # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Score Motor Task");NULL},by=.(IDjoueur)] # # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Score Sensory Task");NULL},by=.(IDjoueur)] # # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Score Logical Task");NULL},by=.(IDjoueur)] # # #

{r detect.outliers.dda.exploit.cs, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS EXPLOIT DDA # #------------------------------------------------------ # # DDA Exploit : Win/Fail delta sum max # # DTDescM = DTM[,.(type="Moteur",var=max(cumulDeltaMise)),by=IDjoueur] # # DTDescS = DTS[,.(type="Sensoriel",var=max(cumulDeltaMise)),by=IDjoueur] # # DTDescL = DTL[,.(type="Logique",var=max(cumulDeltaMise)),by=IDjoueur] # # # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win/Fail delta sum max"); # # # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # # print(paste("Outliers CS EXPLOIT DDA:",toString(outliersLoc$id))) # # # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Motor Task");NULL},by=.(IDjoueur)] # # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Sensory Task");NULL},by=.(IDjoueur)] # # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Logical Task");NULL},by=.(IDjoueur)] #

{r detect.outliers.summary.cs, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUMMARY # #------------------------------------------------------ # print(paste("Total number of outliers: ",toString(nrow(unique(outliers,by="id"))))) # print(paste("Total number of outliers motor task: ",toString(nrow(unique(outliers[type=="Moteur"],by="id"))))) # print(paste("Total number of outliers perceptive task: ",toString(nrow(unique(outliers[type=="Logique"],by="id"))))) # print(paste("Total number of outliers logical task: ",toString(nrow(unique(outliers[type=="Sensoriel"],by="id"))))) #

{r remove.outliers.cs, echo=FALSE} # #------------------------------------------------------ # # REMOVING OUTLIERS FROM TABLES # #------------------------------------------------------ # # removing all outliers and creating a new file only for Confidence Scale Outliers # DTM <- DTM[!IDjoueur %in% unlist(outliers[type=="Moteur"]$id)] # DTS <- DTS[!IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id)] # DTL <- DTL[!IDjoueur %in% unlist(outliers[type=="Logique"]$id)] # DTConfidenceScale <- data.table() # DTConfidenceScale <- rbind(DTConfidenceScale,DTL) # DTConfidenceScale <- rbind(DTConfidenceScale,DTM) # DTConfidenceScale <- rbind(DTConfidenceScale,DTS) #

Modeling difficulties

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1953.7   1975.3   -972.8   1945.7     1620 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1396 -0.7500  0.2888  0.7385  2.8481 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5631   0.7504  
## Number of obs: 1624, groups:  IDjoueur, 56
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.0298     0.1873  -5.499 3.83e-08 ***
## difficulty    2.9618     0.2146  13.803  < 2e-16 ***
## timeNorm     -0.5280     0.2020  -2.614  0.00895 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.539       
## timeNorm   -0.571 -0.009
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0      1624         0 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.050110  
##  1st Qu.:-0.438217  
##  Median :-0.118832  
##  Mean   :-0.002364  
##  3rd Qu.: 0.296005  
##  Max.   : 1.658440  
## [1] "Intercept: -1.03 3.8e-08 ***"
## [1] "Difficulty: 2.96 2.4e-43 ***"
## [1] "Time: -0.528 0.009 **"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.29"
## [1] "Cross Val: 0.68"
## [1] "AIC: 2000"
##         0%        25%        50%        75%       100% 
## -1.6584395 -0.2960052  0.1188317  0.4382172  1.0501105

##         0%        25%        50%        75%       100% 
## -1.6584395 -0.2960052  0.1188317  0.4382172  1.0501105

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1261.1   1282.7   -626.5   1253.1     1620 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.3943 -0.3586  0.1131  0.3536  6.6338 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.7241   0.8509  
## Number of obs: 1624, groups:  IDjoueur, 56
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.3288     0.2583 -12.885   <2e-16 ***
## difficulty    8.2778     0.4068  20.346   <2e-16 ***
## timeNorm     -0.2933     0.2674  -1.097    0.273    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.650       
## timeNorm   -0.519 -0.046
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 2.21089 (tol =
## 0.001, component 1)
## The result is correct only if all data used by the model has not changed since model was fitted.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 2.21089 (tol =
## 0.001, component 1)
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0         0      1624 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.6765404  
##  1st Qu.:-0.4435738  
##  Median : 0.0778425  
##  Mean   :-0.0007671  
##  3rd Qu.: 0.4353921  
##  Max.   : 1.5192471  
## [1] "Intercept: -3.33 5.5e-38 ***"
## [1] "Difficulty: 8.28 5e-92 ***"
## [1] "Time: -0.293 0.27 :("
## [1] "R2 fixed: 0.34"
## [1] "R2 mixed: 0.44"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1300"
##          0%         25%         50%         75%        100% 
## -1.51924712 -0.43539206 -0.07784249  0.44357377  1.67654045

##          0%         25%         50%         75%        100% 
## -1.51924712 -0.43539206 -0.07784249  0.44357377  1.67654045

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1552.8   1574.4   -772.4   1544.8     1649 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.0811 -0.4934 -0.1180  0.4990  5.2065 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.53     1.237   
## Number of obs: 1653, groups:  IDjoueur, 57
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.7716     0.2500  -7.087 1.37e-12 ***
## difficulty    5.7158     0.3070  18.615  < 2e-16 ***
## timeNorm     -2.1395     0.2486  -8.608  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.487       
## timeNorm   -0.373 -0.253
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##      1653         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.8176657  
##  1st Qu.:-0.7404031  
##  Median :-0.2056618  
##  Mean   :-0.0000472  
##  3rd Qu.: 0.7132065  
##  Max.   : 3.1485721  
## [1] "Intercept: -1.77 1.4e-12 ***"
## [1] "Difficulty: 5.72 2.4e-77 ***"
## [1] "Time: -2.14 7.5e-18 ***"
## [1] "R2 fixed: 0.39"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.79"
## [1] "AIC: 1600"
##         0%        25%        50%        75%       100% 
## -3.1485721 -0.7132065  0.2056618  0.7404031  1.8176657

##         0%        25%        50%        75%       100% 
## -3.1485721 -0.7132065  0.2056618  0.7404031  1.8176657

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3815, p-value = 0.1671
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1442117

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.68759, p-value = 0.4917
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.07199342

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.36057, p-value = 0.7184
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.0374431

Playing board games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.86453, p-value = 0.3873
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.08913015

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.48979, p-value = 0.6243
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.05061255

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.75722, p-value = 0.4489
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.07770109

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02429648
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.4833, p-value = 0.01302
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.3393258 
## 
## [1] "self.eff.on.level.s 0.34 0.013 *"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.69753, p-value = 0.4855
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.09334332

Risk aversion and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.5679, p-value = 0.1169
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1554335

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.1214, p-value = 0.03389
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2101231 
## 
## [1] "risk.av.on.level.s 0.21 0.034 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.175, p-value = 0.24
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1154221

Age and level for each task

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.97478, p-value = 0.3297
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.09369113
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.2162, p-value = 0.02668
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2137687 
## 
## [1] "age.on.level.s 0.21 0.027 *"
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.1924, p-value = 0.2331
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1137751

Sex and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.1404, p-value = 0.03233
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2377395 
## 
## [1] "sexe.on.level.m -0.24 0.032 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.077873, p-value = 0.9379
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##          tau 
## -0.008649769

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.20601, p-value = 0.8368
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.0226739

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 220, p-value = 0.03213
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.82775747 -0.05457213
## sample estimates:
## difference in location 
##             -0.4558716 
## 
## [1] "sexe.on.level.m.2 -0.46 0.032 * mean(A): 0.15 mean(B): -0.31"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 347, p-value = 0.9453
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.4361429  0.4780691
## sample estimates:
## difference in location 
##            -0.01100307

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 339, p-value = 0.845
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.7708044  0.5990311
## sample estimates:
## difference in location 
##            -0.02530146

Subjective difficulty and play habits

Playing video game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.17243, p-value = 0.8631
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.01031151

Playing board game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -3.4014, p-value = 0.0006705
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## -0.200843 
## 
## [1] "pbg.on.error -0.2 0.00067 ***"

In game level and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2292, p-value = 0.219
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.06405096

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.79156, p-value = 0.4286
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07272727

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.50886, p-value = 0.6108
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04675325

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.56448, p-value = 0.5724
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.05137845

Sex and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 3.6947, p-value = 0.0002202
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2347598 
## 
## [1] "sexe.on.error 0.23 0.00022 ***"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.9474, p-value = 0.05149
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##      tau 
## 0.216304 
## 
## [1] "sexe.on.error.m 0.22 0.051 ."

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.3448, p-value = 0.01903
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2604541 
## 
## [1] "sexe.on.error.s 0.26 0.019 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.0601, p-value = 0.03939
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##      tau 
## 0.226739 
## 
## [1] "sexe.on.error.l 0.23 0.039 *"

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 4236, p-value = 0.0002216
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.04230885 0.12402020
## sample estimates:
## difference in location 
##             0.08554301 
## 
## [1] "sexe.on.error.2 0.086 0.00022 *** mean(A): -0.1 mean(B): -0.011"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 453, p-value = 0.05195
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.0002022052  0.1424522266
## sample estimates:
## difference in location 
##             0.07876811 
## 
## [1] "sexe.on.error.m.2 0.079 0.052 . mean(A): -0.096 mean(B): -0.012"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 487, p-value = 0.01851
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.01536563 0.15935580
## sample estimates:
## difference in location 
##             0.09651898 
## 
## [1] "sexe.on.error.s.2 0.097 0.019 * mean(A): -0.1 mean(B): -0.0041"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 471, p-value = 0.03941
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.003843216 0.147684185
## sample estimates:
## difference in location 
##             0.08088642 
## 
## [1] "sexe.on.error.l.2 0.081 0.039 * mean(A): -0.1 mean(B): -0.016"

Risk aversion and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.96322, p-value = 0.3354
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.05451705

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.33391, p-value = 0.7384
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.03310158

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.27607, p-value = 0.7825
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02734478

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.0476, p-value = 0.2948
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1029065

Self efficacy and subjective difficulty error

## Warning: Removed 84 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.6413, p-value = 0.00826
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2031367 
## 
## [1] "self.eff.on.error -0.2 0.0083 **"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.6463, p-value = 0.09969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2240675 
## 
## [1] "self.eff.on.error -0.22 0.1 :("
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.3311, p-value = 0.1832
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1818786
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.527, p-value = 0.1268
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2043462

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        0.00840 51     0.73 :(
##  2:      0.09375        0.02700 56     0.12 :(
##  3:      0.15625       -0.01300 57     0.44 :(
##  4:      0.21875        0.04300 58     0.14 :(
##  5:      0.28125       -0.04300 57     0.23 :(
##  6:      0.34375        0.01300 57      0.8 :(
##  7:      0.40625        0.01500 56     0.67 :(
##  8:      0.46875       -0.02500 57      0.5 :(
##  9:      0.53125       -0.00068 55     0.96 :(
## 10:      0.59375        0.00150 58     0.85 :(
## 11:      0.65625       -0.06100 58     0.046 *
## 12:      0.71875       -0.11000 58 3.2e-05 ***
## 13:      0.78125       -0.16000 56 3.7e-08 ***
## 14:      0.84375       -0.19000 56 3.9e-09 ***
## 15:      0.90625       -0.20000 57 4.9e-11 ***
## 16:      0.96875       -0.17000 57   5e-11 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 51     0.73 :(
##  2: 56     0.12 :(
##  3: 57     0.44 :(
##  4: 58     0.14 :(
##  5: 57     0.23 :(
##  6: 57      0.8 :(
##  7: 56     0.67 :(
##  8: 57      0.5 :(
##  9: 55     0.96 :(
## 10: 58     0.85 :(
## 11: 58     0.046 *
## 12: 58 3.2e-05 ***
## 13: 56 3.7e-08 ***
## 14: 56 3.9e-09 ***
## 15: 57 4.9e-11 ***
## 16: 57   5e-11 ***
## [1] 56.5
## [1] 1.71

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0130 35     0.73 :(
##  2:      0.09375        -0.0045 37     0.99 :(
##  3:      0.15625        -0.0670 43     0.24 :(
##  4:      0.21875         0.0240 41      0.6 :(
##  5:      0.28125        -0.0430 39     0.41 :(
##  6:      0.34375         0.0130 39     0.68 :(
##  7:      0.40625         0.0460 40      0.3 :(
##  8:      0.46875         0.0240 38     0.76 :(
##  9:      0.53125        -0.0220 40     0.91 :(
## 10:      0.59375        -0.0100 41      0.9 :(
## 11:      0.65625        -0.0630 35     0.093 .
## 12:      0.71875        -0.1500 38 0.00024 ***
## 13:      0.78125        -0.2000 38 0.00015 ***
## 14:      0.84375        -0.2400 26 2.7e-05 ***
## 15:      0.90625        -0.1900 30 1.6e-06 ***
## 16:      0.96875        -0.1500 19 0.00013 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 35     0.73 :(
##  2: 37     0.99 :(
##  3: 43     0.24 :(
##  4: 41      0.6 :(
##  5: 39     0.41 :(
##  6: 39     0.68 :(
##  7: 40      0.3 :(
##  8: 38     0.76 :(
##  9: 40     0.91 :(
## 10: 41      0.9 :(
## 11: 35     0.093 .
## 12: 38 0.00024 ***
## 13: 38 0.00015 ***
## 14: 26 2.7e-05 ***
## 15: 30 1.6e-06 ***
## 16: 19 0.00013 ***
## [1] 36.2
## [1] 6.26

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 30     0.14 :(
##  2:      0.09375         0.0400 33     0.089 .
##  3:      0.15625         0.0580 30     0.51 :(
##  4:      0.21875        -0.0044 38     0.84 :(
##  5:      0.28125        -0.0670 35     0.18 :(
##  6:      0.34375        -0.0530 36     0.48 :(
##  7:      0.40625        -0.0380 36     0.45 :(
##  8:      0.46875        -0.0400 35     0.33 :(
##  9:      0.53125         0.0880 36     0.072 .
## 10:      0.59375         0.0670 35      0.2 :(
## 11:      0.65625        -0.0700 36     0.17 :(
## 12:      0.71875        -0.0760 38      0.1 :(
## 13:      0.78125        -0.0670 39     0.011 *
## 14:      0.84375        -0.1500 37 3.4e-05 ***
## 15:      0.90625        -0.2000 35 2.5e-07 ***
## 16:      0.96875        -0.1900 33 5.5e-07 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 30     0.14 :(
##  2: 33     0.089 .
##  3: 30     0.51 :(
##  4: 38     0.84 :(
##  5: 35     0.18 :(
##  6: 36     0.48 :(
##  7: 36     0.45 :(
##  8: 35     0.33 :(
##  9: 36     0.072 .
## 10: 35      0.2 :(
## 11: 36     0.17 :(
## 12: 38      0.1 :(
## 13: 39     0.011 *
## 14: 37 3.4e-05 ***
## 15: 35 2.5e-07 ***
## 16: 33 5.5e-07 ***
## [1] 35.1
## [1] 2.58

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375        -0.0220  9     0.72 :(
##  3:      0.15625        -0.0041 12        1 :(
##  4:      0.21875         0.0055 11        1 :(
##  5:      0.28125         0.0760 11     0.35 :(
##  6:      0.34375         0.1100  9     0.41 :(
##  7:      0.40625         0.0940 12     0.22 :(
##  8:      0.46875        -0.0400 16     0.66 :(
##  9:      0.53125        -0.1400 15      0.2 :(
## 10:      0.59375        -0.1200 13     0.21 :(
## 11:      0.65625        -0.1000 15     0.13 :(
## 12:      0.71875        -0.1500 14     0.033 *
## 13:      0.78125        -0.1900 15   0.0029 **
## 14:      0.84375        -0.1700 18     0.013 *
## 15:      0.90625        -0.1500 18 0.00021 ***
## 16:      0.96875        -0.2200 18 0.00021 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  9     0.72 :(
##  2: 12        1 :(
##  3: 11        1 :(
##  4: 11     0.35 :(
##  5:  9     0.41 :(
##  6: 12     0.22 :(
##  7: 16     0.66 :(
##  8: 15      0.2 :(
##  9: 13     0.21 :(
## 10: 15     0.13 :(
## 11: 14     0.033 *
## 12: 15   0.0029 **
## 13: 18     0.013 *
## 14: 18 0.00021 ***
## 15: 18 0.00021 ***
## [1] 13.7
## [1] 3.06
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Motor task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375         -0.094  8     0.21 :(
##  3:      0.15625         -0.099 26     0.015 *
##  4:      0.21875         -0.076 40   0.0065 **
##  5:      0.28125         -0.067 45     0.055 .
##  6:      0.34375         -0.058 47     0.21 :(
##  7:      0.40625         -0.013 49      0.8 :(
##  8:      0.46875          0.031 49     0.73 :(
##  9:      0.53125          0.076 51     0.15 :(
## 10:      0.59375          0.025 51     0.55 :(
## 11:      0.65625         -0.013 53     0.45 :(
## 12:      0.71875         -0.052 51     0.079 .
## 13:      0.78125         -0.067 44     0.029 *
## 14:      0.84375         -0.094 27   0.0073 **
## 15:      0.90625         -0.078 14 0.00076 ***
## 16:      0.96875         -0.110  6     0.034 *
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  8     0.21 :(
##  2: 26     0.015 *
##  3: 40   0.0065 **
##  4: 45     0.055 .
##  5: 47     0.21 :(
##  6: 49      0.8 :(
##  7: 49     0.73 :(
##  8: 51     0.15 :(
##  9: 51     0.55 :(
## 10: 53     0.45 :(
## 11: 51     0.079 .
## 12: 44     0.029 *
## 13: 27   0.0073 **
## 14: 14 0.00076 ***
## 15:  6     0.034 *
## [1] 37.4
## [1] 16.7
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n     pval
##  1:      0.03125             NA  0       NA
##  2:      0.09375        -0.0940  8  0.21 :(
##  3:      0.15625        -0.1200 24 0.005 **
##  4:      0.21875        -0.0760 26  0.031 *
##  5:      0.28125        -0.0670 25  0.12 :(
##  6:      0.34375         0.0130 26   0.8 :(
##  7:      0.40625         0.0320 25  0.67 :(
##  8:      0.46875         0.0880 24  0.14 :(
##  9:      0.53125         0.0760 23  0.21 :(
## 10:      0.59375         0.0970 24  0.038 *
## 11:      0.65625         0.0081 25  0.94 :(
## 12:      0.71875        -0.0470 22  0.078 .
## 13:      0.78125        -0.1000 15  0.26 :(
## 14:      0.84375             NA  0       NA
## 15:      0.90625             NA  0       NA
## 16:      0.96875             NA  0       NA
## [1] "mean and sd of nb players per bin"
##     nb     pval
##  1:  8  0.21 :(
##  2: 24 0.005 **
##  3: 26  0.031 *
##  4: 25  0.12 :(
##  5: 26   0.8 :(
##  6: 25  0.67 :(
##  7: 24  0.14 :(
##  8: 23  0.21 :(
##  9: 24  0.038 *
## 10: 25  0.94 :(
## 11: 22  0.078 .
## 12: 15  0.26 :(
## [1] 22.2
## [1] 5.36
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  0        NA
##  3:      0.15625         0.2000  2      1 :(
##  4:      0.21875        -0.2200 14   0.15 :(
##  5:      0.28125        -0.0990 20   0.38 :(
##  6:      0.34375        -0.1600 20    0.08 .
##  7:      0.40625        -0.0490 22   0.31 :(
##  8:      0.46875        -0.0160 21   0.63 :(
##  9:      0.53125         0.1400 21 0.0048 **
## 10:      0.59375         0.0130 21   0.86 :(
## 11:      0.65625        -0.0130 21   0.94 :(
## 12:      0.71875         0.0430 22   0.43 :(
## 13:      0.78125        -0.0099 21   0.75 :(
## 14:      0.84375        -0.0940 19   0.017 *
## 15:      0.90625             NA  6        NA
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  2      1 :(
##  2: 14   0.15 :(
##  3: 20   0.38 :(
##  4: 20    0.08 .
##  5: 22   0.31 :(
##  6: 21   0.63 :(
##  7: 21 0.0048 **
##  8: 21   0.86 :(
##  9: 21   0.94 :(
## 10: 22   0.43 :(
## 11: 21   0.75 :(
## 12: 19   0.017 *
## [1] 18.7
## [1] 5.66
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 1      NA
##  7:      0.40625         -0.049 2    1 :(
##  8:      0.46875         -0.180 4 0.58 :(
##  9:      0.53125         -0.400 7 0.071 .
## 10:      0.59375         -0.290 6 0.14 :(
## 11:      0.65625         -0.230 7 0.16 :(
## 12:      0.71875         -0.250 7 0.047 *
## 13:      0.78125         -0.180 8 0.023 *
## 14:      0.84375         -0.110 8 0.29 :(
## 15:      0.90625         -0.110 8 0.013 *
## 16:      0.96875         -0.110 6 0.034 *
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  2    1 :(
##  2:  4 0.58 :(
##  3:  7 0.071 .
##  4:  6 0.14 :(
##  5:  7 0.16 :(
##  6:  7 0.047 *
##  7:  8 0.023 *
##  8:  8 0.29 :(
##  9:  8 0.013 *
## 10:  6 0.034 *
## [1] 6.3
## [1] 1.95
## Warning: Removed 6 rows containing missing values (geom_point).
## Warning: Removed 6 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 44     0.033 *
##  2:      0.09375         -0.094 53     0.014 *
##  3:      0.15625         -0.071 48     0.046 *
##  4:      0.21875         -0.040 40     0.21 :(
##  5:      0.28125         -0.067 38     0.42 :(
##  6:      0.34375         -0.058 36     0.21 :(
##  7:      0.40625         -0.049 37     0.53 :(
##  8:      0.46875         -0.110 37     0.033 *
##  9:      0.53125         -0.140 30     0.027 *
## 10:      0.59375         -0.170 33     0.029 *
## 11:      0.65625         -0.085 34     0.029 *
## 12:      0.71875         -0.150 34   0.0034 **
## 13:      0.78125         -0.210 38 0.00063 ***
## 14:      0.84375         -0.150 45 8.4e-05 ***
## 15:      0.90625         -0.170 53 1.7e-10 ***
## 16:      0.96875         -0.140 56 6.3e-11 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 44     0.033 *
##  2: 53     0.014 *
##  3: 48     0.046 *
##  4: 40     0.21 :(
##  5: 38     0.42 :(
##  6: 36     0.21 :(
##  7: 37     0.53 :(
##  8: 37     0.033 *
##  9: 30     0.027 *
## 10: 33     0.029 *
## 11: 34     0.029 *
## 12: 34   0.0034 **
## 13: 38 0.00063 ***
## 14: 45 8.4e-05 ***
## 15: 53 1.7e-10 ***
## 16: 56 6.3e-11 ***
## [1] 41
## [1] 7.94

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 19     0.69 :(
##  2:      0.09375        -0.0940 18    0.002 **
##  3:      0.15625        -0.1600 17     0.061 .
##  4:      0.21875        -0.0045 10     0.61 :(
##  5:      0.28125        -0.0670 16     0.51 :(
##  6:      0.34375        -0.2000 12     0.064 .
##  7:      0.40625        -0.1900 12      0.03 *
##  8:      0.46875        -0.2500 15     0.011 *
##  9:      0.53125        -0.3000 11     0.027 *
## 10:      0.59375        -0.2400 12     0.031 *
## 11:      0.65625        -0.1400 12     0.077 .
## 12:      0.71875        -0.3600 11   0.0038 **
## 13:      0.78125        -0.3500 12     0.011 *
## 14:      0.84375        -0.2400 13     0.014 *
## 15:      0.90625        -0.1600 18 0.00019 ***
## 16:      0.96875        -0.1500 19 0.00012 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 19     0.69 :(
##  2: 18    0.002 **
##  3: 17     0.061 .
##  4: 10     0.61 :(
##  5: 16     0.51 :(
##  6: 12     0.064 .
##  7: 12      0.03 *
##  8: 15     0.011 *
##  9: 11     0.027 *
## 10: 12     0.031 *
## 11: 12     0.077 .
## 12: 11   0.0038 **
## 13: 12     0.011 *
## 14: 13     0.014 *
## 15: 18 0.00019 ***
## 16: 19 0.00012 ***
## [1] 14.2
## [1] 3.17

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 25   0.0082 **
##  2:      0.09375         -0.094 27     0.31 :(
##  3:      0.15625         -0.120 21     0.058 .
##  4:      0.21875         -0.076 22      0.2 :(
##  5:      0.28125         -0.140 15     0.51 :(
##  6:      0.34375          0.013 19     0.95 :(
##  7:      0.40625          0.022 20     0.72 :(
##  8:      0.46875         -0.110 17     0.25 :(
##  9:      0.53125         -0.100 15     0.44 :(
## 10:      0.59375         -0.150 16     0.31 :(
## 11:      0.65625         -0.160 17     0.17 :(
## 12:      0.71875         -0.076 16     0.15 :(
## 13:      0.78125         -0.067 21     0.11 :(
## 14:      0.84375         -0.130 24   0.0066 **
## 15:      0.90625         -0.190 27 4.7e-06 ***
## 16:      0.96875         -0.140 27 5.5e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 25   0.0082 **
##  2: 27     0.31 :(
##  3: 21     0.058 .
##  4: 22      0.2 :(
##  5: 15     0.51 :(
##  6: 19     0.95 :(
##  7: 20     0.72 :(
##  8: 17     0.25 :(
##  9: 15     0.44 :(
## 10: 16     0.31 :(
## 11: 17     0.17 :(
## 12: 16     0.15 :(
## 13: 21     0.11 :(
## 14: 24   0.0066 **
## 15: 27 4.7e-06 ***
## 16: 27 5.5e-06 ***
## [1] 20.6
## [1] 4.4

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375        -0.0220  8   0.94 :(
##  3:      0.15625         0.0220 10   0.61 :(
##  4:      0.21875         0.0260  8      1 :(
##  5:      0.28125         0.0400  7    0.8 :(
##  6:      0.34375        -0.0220  5   0.78 :(
##  7:      0.40625         0.1200  5   0.44 :(
##  8:      0.46875         0.2100  5   0.19 :(
##  9:      0.53125        -0.0250  4   0.62 :(
## 10:      0.59375        -0.0220  5      1 :(
## 11:      0.65625        -0.0130  5   0.78 :(
## 12:      0.71875         0.0063  7      1 :(
## 13:      0.78125        -0.2100  5   0.19 :(
## 14:      0.84375        -0.0580  8   0.29 :(
## 15:      0.90625        -0.1700  8   0.014 *
## 16:      0.96875        -0.1200 10 0.0059 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  8   0.94 :(
##  2: 10   0.61 :(
##  3:  8      1 :(
##  4:  7    0.8 :(
##  5:  5   0.78 :(
##  6:  5   0.44 :(
##  7:  5   0.19 :(
##  8:  4   0.62 :(
##  9:  5      1 :(
## 10:  5   0.78 :(
## 11:  7      1 :(
## 12:  5   0.19 :(
## 13:  8   0.29 :(
## 14:  8   0.014 *
## 15: 10 0.0059 **
## [1] 6.67
## [1] 1.95
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0130 40     0.95 :(
##  2:      0.09375         0.1200 44     0.015 *
##  3:      0.15625         0.0580 46     0.19 :(
##  4:      0.21875         0.1900 48   0.0019 **
##  5:      0.28125         0.1100 36      0.2 :(
##  6:      0.34375         0.0850 44     0.078 .
##  7:      0.40625         0.0560 43     0.11 :(
##  8:      0.46875        -0.0045 42      0.9 :(
##  9:      0.53125        -0.0310 42     0.45 :(
## 10:      0.59375        -0.0220 46     0.94 :(
## 11:      0.65625        -0.0850 40     0.11 :(
## 12:      0.71875        -0.1500 44     0.016 *
## 13:      0.78125        -0.1400 47 0.00089 ***
## 14:      0.84375        -0.2700 48 1.5e-08 ***
## 15:      0.90625        -0.2400 43 1.1e-08 ***
## 16:      0.96875        -0.3000 29 2.7e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 40     0.95 :(
##  2: 44     0.015 *
##  3: 46     0.19 :(
##  4: 48   0.0019 **
##  5: 36      0.2 :(
##  6: 44     0.078 .
##  7: 43     0.11 :(
##  8: 42      0.9 :(
##  9: 42     0.45 :(
## 10: 46     0.94 :(
## 11: 40     0.11 :(
## 12: 44     0.016 *
## 13: 47 0.00089 ***
## 14: 48 1.5e-08 ***
## 15: 43 1.1e-08 ***
## 16: 29 2.7e-06 ***
## [1] 42.6
## [1] 4.83

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0130 28     0.75 :(
##  2:      0.09375         0.0490 28     0.33 :(
##  3:      0.15625         0.0580 27     0.69 :(
##  4:      0.21875         0.1900 26   0.0095 **
##  5:      0.28125        -0.0074 18        1 :(
##  6:      0.34375         0.0340 24     0.56 :(
##  7:      0.40625         0.1700 22     0.034 *
##  8:      0.46875         0.1000 21     0.25 :(
##  9:      0.53125        -0.0310 23     0.66 :(
## 10:      0.59375        -0.0760 24     0.15 :(
## 11:      0.65625        -0.1200 17     0.042 *
## 12:      0.71875        -0.1100 21      0.07 .
## 13:      0.78125        -0.1700 23     0.019 *
## 14:      0.84375        -0.2700 21 0.00015 ***
## 15:      0.90625        -0.2600 16 0.00046 ***
## 16:      0.96875             NA  1          NA
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 28     0.75 :(
##  2: 28     0.33 :(
##  3: 27     0.69 :(
##  4: 26   0.0095 **
##  5: 18        1 :(
##  6: 24     0.56 :(
##  7: 22     0.034 *
##  8: 21     0.25 :(
##  9: 23     0.66 :(
## 10: 24     0.15 :(
## 11: 17     0.042 *
## 12: 21      0.07 .
## 13: 23     0.019 *
## 14: 21 0.00015 ***
## 15: 16 0.00046 ***
## [1] 22.6
## [1] 3.76
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 12     0.65 :(
##  2:      0.09375          0.330 15     0.011 *
##  3:      0.15625          0.240 16     0.052 .
##  4:      0.21875          0.210 18      0.06 .
##  5:      0.28125          0.150 13     0.14 :(
##  6:      0.34375          0.110 14     0.13 :(
##  7:      0.40625         -0.049 15     0.55 :(
##  8:      0.46875         -0.040 13     0.33 :(
##  9:      0.53125         -0.016 11        1 :(
## 10:      0.59375          0.160 15     0.031 *
## 11:      0.65625         -0.085 14     0.41 :(
## 12:      0.71875         -0.150 17      0.1 :(
## 13:      0.78125         -0.091 17     0.087 .
## 14:      0.84375         -0.240 19 0.00037 ***
## 15:      0.90625         -0.260 18 0.00021 ***
## 16:      0.96875         -0.330 18 0.00021 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 12     0.65 :(
##  2: 15     0.011 *
##  3: 16     0.052 .
##  4: 18      0.06 .
##  5: 13     0.14 :(
##  6: 14     0.13 :(
##  7: 15     0.55 :(
##  8: 13     0.33 :(
##  9: 11        1 :(
## 10: 15     0.031 *
## 11: 14     0.41 :(
## 12: 17      0.1 :(
## 13: 17     0.087 .
## 14: 19 0.00037 ***
## 15: 18 0.00021 ***
## 16: 18 0.00021 ***
## [1] 15.3
## [1] 2.39

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  1        NA
##  3:      0.15625             NA  3        NA
##  4:      0.21875          0.120  4   0.62 :(
##  5:      0.28125          0.150  5   0.27 :(
##  6:      0.34375          0.160  6    0.2 :(
##  7:      0.40625          0.022  6   0.29 :(
##  8:      0.46875         -0.040  8   0.62 :(
##  9:      0.53125         -0.100  8   0.44 :(
## 10:      0.59375         -0.170  7   0.44 :(
## 11:      0.65625          0.058  9   0.72 :(
## 12:      0.71875         -0.076  6   0.84 :(
## 13:      0.78125         -0.190  7   0.15 :(
## 14:      0.84375         -0.340  8   0.042 *
## 15:      0.90625         -0.170  9 0.0091 **
## 16:      0.96875         -0.310 10  0.002 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  4   0.62 :(
##  2:  5   0.27 :(
##  3:  6    0.2 :(
##  4:  6   0.29 :(
##  5:  8   0.62 :(
##  6:  8   0.44 :(
##  7:  7   0.44 :(
##  8:  9   0.72 :(
##  9:  6   0.84 :(
## 10:  7   0.15 :(
## 11:  8   0.042 *
## 12:  9 0.0091 **
## 13: 10  0.002 **
## [1] 7.15
## [1] 1.72
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.85521  -0.20000   0.03999   0.20805   0.69174  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.02757    0.02336  -1.180   0.2381   
## timeNorm     0.03482    0.02460   1.416   0.1570   
## obj.diff    -0.08659    0.03066  -2.824   0.0048 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07429011)
## 
##     Null deviance: 121.29  on 1623  degrees of freedom
## Residual deviance: 120.42  on 1621  degrees of freedom
## AIC: 391.67
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78610  -0.11567   0.04559   0.11403   0.81494  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.001581   0.016841   0.094    0.925    
## timeNorm     0.009074   0.022514   0.403    0.687    
## obj.diff    -0.212404   0.017421 -12.192   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06402259)
## 
##     Null deviance: 113.31  on 1623  degrees of freedom
## Residual deviance: 103.78  on 1621  degrees of freedom
## AIC: 150.11
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.72093  -0.22888   0.01713   0.22812   0.65860  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.16199    0.02321   6.980 4.28e-12 ***
## timeNorm     0.03749    0.02872   1.305    0.192    
## obj.diff    -0.46231    0.02441 -18.941  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09806891)
## 
##     Null deviance: 201.49  on 1652  degrees of freedom
## Residual deviance: 161.81  on 1650  degrees of freedom
## AIC: 857.6
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5      0.5089286     0.6008109 -0.07683886 112 0.0057 **
##  2:      4.5      0.4889456     0.5714407 -0.07445527 168 8e-04 ***
##  3:      7.5      0.4863946     0.5416953 -0.04763643 168   0.023 *
##  4:     10.5      0.5008503     0.5401276 -0.03488016 168   0.13 :(
##  5:     13.5      0.4447279     0.5174551 -0.06672273 168 0.0017 **
##  6:     16.5      0.4931973     0.5305272 -0.02102698 168   0.36 :(
##  7:     19.5      0.4736395     0.5315528 -0.04770887 168   0.021 *
##  8:     22.5      0.4455782     0.4897264 -0.03529116 168   0.093 .
##  9:     25.5      0.4464286     0.4805683 -0.02474658 168   0.31 :(
## 10:     28.5      0.4166667     0.4572889 -0.03958163 168   0.083 .
##     time  error.diff shapes
##  1:  1.5 -0.07683886     24
##  2:  4.5 -0.07445527     24
##  3:  7.5 -0.04763643     24
##  4: 10.5 -0.03488016     16
##  5: 13.5 -0.06672273     24
##  6: 16.5 -0.02102698     16
##  7: 19.5 -0.04770887     24
##  8: 22.5 -0.03529116     16
##  9: 25.5 -0.02474658     16
## 10: 28.5 -0.03958163     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4285714     0.5941293 -0.14841500 112 7.1e-09 ***
##  2:      4.5      0.5212585     0.6104788 -0.09151470 168 4.2e-08 ***
##  3:      7.5      0.4379252     0.5299114 -0.09240609 168 1.5e-07 ***
##  4:     10.5      0.4642857     0.5824635 -0.10424800 168 1.8e-11 ***
##  5:     13.5      0.4302721     0.5656294 -0.11814246 168 2.1e-13 ***
##  6:     16.5      0.4064626     0.5333505 -0.11438030 168 4.2e-11 ***
##  7:     19.5      0.4685374     0.5641391 -0.08414982 168 1.9e-08 ***
##  8:     22.5      0.4311224     0.5656705 -0.12484954 168 2.4e-12 ***
##  9:     25.5      0.4923469     0.5874740 -0.09752555 168 7.2e-11 ***
## 10:     28.5      0.4608844     0.5711020 -0.10647805 168 1.2e-10 ***
##     time  error.diff shapes
##  1:  1.5 -0.14841500     24
##  2:  4.5 -0.09151470     24
##  3:  7.5 -0.09240609     24
##  4: 10.5 -0.10424800     24
##  5: 13.5 -0.11814246     24
##  6: 16.5 -0.11438030     24
##  7: 19.5 -0.08414982     24
##  8: 22.5 -0.12484954     24
##  9: 25.5 -0.09752555     24
## 10: 28.5 -0.10647805     24

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n        pval
##  1:      1.5      0.4122807     0.6044431 -0.1751396517 114 1.5e-08 ***
##  2:      4.5      0.5037594     0.6442014 -0.1329386965 171 2.5e-07 ***
##  3:      7.5      0.5037594     0.5633809 -0.0651969309 171   0.0077 **
##  4:     10.5      0.4970760     0.5330653 -0.0461989995 171     0.061 .
##  5:     13.5      0.4761905     0.5198918 -0.0382858444 171     0.15 :(
##  6:     16.5      0.4820384     0.4996879 -0.0306617697 171     0.23 :(
##  7:     19.5      0.4185464     0.4399282 -0.0305348039 171     0.29 :(
##  8:     22.5      0.3918129     0.4071581 -0.0206465199 171     0.47 :(
##  9:     25.5      0.3851295     0.3861396  0.0002090139 171     0.99 :(
## 10:     28.5      0.3792815     0.3521331 -0.0024558459 171     0.93 :(
##     time    error.diff shapes
##  1:  1.5 -0.1751396517     24
##  2:  4.5 -0.1329386965     24
##  3:  7.5 -0.0651969309     24
##  4: 10.5 -0.0461989995     16
##  5: 13.5 -0.0382858444     16
##  6: 16.5 -0.0306617697     16
##  7: 19.5 -0.0305348039     16
##  8: 22.5 -0.0206465199     16
##  9: 25.5  0.0002090139     16
## 10: 28.5 -0.0024558459     16

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7392  -0.2163   0.1243   0.1836   0.6877  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.11142    0.04170   2.672   0.0077 ** 
## timeNorm     0.03093    0.03984   0.776   0.4377    
## obj.diff    -0.38577    0.04240  -9.099   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09827124)
## 
##     Null deviance: 88.057  on 811  degrees of freedom
## Residual deviance: 79.501  on 809  degrees of freedom
## AIC: 425.49
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79431  -0.19982   0.05166   0.19690   0.71997  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.06708    0.01956   3.430 0.000616 ***
## timeNorm     0.04788    0.02328   2.057 0.039863 *  
## obj.diff    -0.28088    0.02172 -12.933  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.08185256)
## 
##     Null deviance: 175.98  on 1971  degrees of freedom
## Residual deviance: 161.17  on 1969  degrees of freedom
## AIC: 665.7
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.76793  -0.18358  -0.05094   0.19544   0.77380  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.04974    0.01661   2.995  0.00277 ** 
## timeNorm     0.02988    0.02152   1.388  0.16523    
## obj.diff    -0.26359    0.02069 -12.743  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07381598)
## 
##     Null deviance: 169.06  on 2116  degrees of freedom
## Residual deviance: 156.05  on 2114  degrees of freedom
## AIC: 495.5
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5688776     0.7865986 -0.20015737 56 5.4e-07 ***
##  2:      4.5      0.6105442     0.7834491 -0.13820329 84 1.2e-05 ***
##  3:      7.5      0.6105442     0.7803283 -0.13482737 84 4.7e-07 ***
##  4:     10.5      0.5986395     0.7476758 -0.13141657 84 4.3e-05 ***
##  5:     13.5      0.6122449     0.7699417 -0.11939121 84   3e-05 ***
##  6:     16.5      0.5561224     0.7334575 -0.13531338 84 1.3e-06 ***
##  7:     19.5      0.5578231     0.7066818 -0.11199360 84   0.0011 **
##  8:     22.5      0.5765306     0.7332058 -0.12337951 84 1.2e-05 ***
##  9:     25.5      0.5238095     0.6897234 -0.13639706 84 1.6e-05 ***
## 10:     28.5      0.5969388     0.6715480 -0.06951463 84   0.0089 **
##     time  error.diff shapes
##  1:  1.5 -0.20015737     24
##  2:  4.5 -0.13820329     24
##  3:  7.5 -0.13482737     24
##  4: 10.5 -0.13141657     24
##  5: 13.5 -0.11939121     24
##  6: 16.5 -0.13531338     24
##  7: 19.5 -0.11199360     24
##  8: 22.5 -0.12337951     24
##  9: 25.5 -0.13639706     24
## 10: 28.5 -0.06951463     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4737395     0.6108572 -0.12391990 136 3.4e-06 ***
##  2:      4.5      0.5455182     0.6756446 -0.11461993 204 1.1e-10 ***
##  3:      7.5      0.4880952     0.5392984 -0.05624395 204   0.0026 **
##  4:     10.5      0.5336134     0.5794722 -0.05472763 204   0.0068 **
##  5:     13.5      0.4894958     0.5707053 -0.07912737 204 8.6e-05 ***
##  6:     16.5      0.5147059     0.5552538 -0.04283132 204     0.043 *
##  7:     19.5      0.5063025     0.5632170 -0.05365583 204   0.0052 **
##  8:     22.5      0.4495798     0.5121017 -0.06880287 204   0.0015 **
##  9:     25.5      0.4922969     0.5224562 -0.04441610 204     0.058 .
## 10:     28.5      0.4656863     0.5059577 -0.05291457 204   0.0066 **
##     time  error.diff shapes
##  1:  1.5 -0.12391990     24
##  2:  4.5 -0.11461993     24
##  3:  7.5 -0.05624395     24
##  4: 10.5 -0.05472763     24
##  5: 13.5 -0.07912737     24
##  6: 16.5 -0.04283132     24
##  7: 19.5 -0.05365583     24
##  8: 22.5 -0.06880287     24
##  9: 25.5 -0.04441610     16
## 10: 28.5 -0.05291457     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.3816047     0.5179021 -0.11947643 146 9.7e-07 ***
##  2:      4.5      0.4259622     0.4798159 -0.05945139 219   0.0048 **
##  3:      7.5      0.4135682     0.4602904 -0.04895745 219     0.014 *
##  4:     10.5      0.4018265     0.4508328 -0.05577496 219   0.0029 **
##  5:     13.5      0.3522505     0.4098663 -0.06186834 219 0.00068 ***
##  6:     16.5      0.3737769     0.4077438 -0.03978670 219     0.027 *
##  7:     19.5      0.3639922     0.3883398 -0.03472712 219     0.041 *
##  8:     22.5      0.3385519     0.3692817 -0.03631424 219     0.049 *
##  9:     25.5      0.3613829     0.3696034 -0.01725788 219     0.38 :(
## 10:     28.5      0.3065884     0.3349728 -0.04447969 219     0.018 *
##     time  error.diff shapes
##  1:  1.5 -0.11947643     24
##  2:  4.5 -0.05945139     24
##  3:  7.5 -0.04895745     24
##  4: 10.5 -0.05577496     24
##  5: 13.5 -0.06186834     24
##  6: 16.5 -0.03978670     24
##  7: 19.5 -0.03472712     24
##  8: 22.5 -0.03631424     24
##  9: 25.5 -0.01725788     16
## 10: 28.5 -0.04447969     24

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.77782  -0.16141   0.07773   0.18154   0.65784  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.462332   0.119085  -3.882 0.000135 ***
## timeNorm     0.004994   0.071725   0.070 0.944555    
## obj.diff     0.309216   0.135862   2.276 0.023773 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09109535)
## 
##     Null deviance: 21.338  on 231  degrees of freedom
## Residual deviance: 20.861  on 229  degrees of freedom
## AIC: 107.53
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean error.diff  n        pval
##  1:      1.5      0.6339286     0.8544830 -0.1813070 16   0.0013 **
##  2:      4.5      0.5773810     0.7995145 -0.1900501 24 0.00057 ***
##  3:      7.5      0.5714286     0.7551085 -0.1583598 24   0.0043 **
##  4:     10.5      0.5892857     0.7836615 -0.1770491 24   0.0011 **
##  5:     13.5      0.6071429     0.8240112 -0.1620422 24   0.0018 **
##  6:     16.5      0.4821429     0.7818411 -0.2673553 24 0.00028 ***
##  7:     19.5      0.5000000     0.7263256 -0.2097781 24   0.0096 **
##  8:     22.5      0.6130952     0.7654436 -0.1099361 24     0.11 :(
##  9:     25.5      0.5119048     0.7908307 -0.2703569 24 0.00018 ***
## 10:     28.5      0.5476190     0.7394768 -0.1501698 24   0.0087 **
##     time error.diff shapes
##  1:  1.5 -0.1813070     24
##  2:  4.5 -0.1900501     24
##  3:  7.5 -0.1583598     24
##  4: 10.5 -0.1770491     24
##  5: 13.5 -0.1620422     24
##  6: 16.5 -0.2673553     24
##  7: 19.5 -0.2097781     24
##  8: 22.5 -0.1099361     16
##  9: 25.5 -0.2703569     24
## 10: 28.5 -0.1501698     24
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.81780  -0.19138   0.04321   0.17708   0.68822  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.13079    0.04059  -3.222  0.00134 **
## timeNorm     0.07430    0.03785   1.963  0.05008 . 
## obj.diff     0.09494    0.05455   1.740  0.08228 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06868557)
## 
##     Null deviance: 44.015  on 637  degrees of freedom
## Residual deviance: 43.615  on 635  degrees of freedom
## AIC: 106.86
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n    pval
##  1:      1.5      0.5227273     0.6251419 -0.087913622 44 0.062 .
##  2:      4.5      0.5432900     0.6224524 -0.067787288 66 0.053 .
##  3:      7.5      0.5064935     0.5482212 -0.033170955 66 0.34 :(
##  4:     10.5      0.5519481     0.5744464 -0.017320378 66  0.7 :(
##  5:     13.5      0.5086580     0.5455378 -0.027725556 66 0.47 :(
##  6:     16.5      0.5519481     0.5560045  0.008925402 66 0.85 :(
##  7:     19.5      0.5519481     0.5704673 -0.010678355 66 0.76 :(
##  8:     22.5      0.4307359     0.5060978 -0.079405018 66 0.035 *
##  9:     25.5      0.4870130     0.4999714 -0.012031106 66 0.76 :(
## 10:     28.5      0.4870130     0.5016324 -0.017813543 66 0.61 :(
##     time   error.diff shapes
##  1:  1.5 -0.087913622     16
##  2:  4.5 -0.067787288     16
##  3:  7.5 -0.033170955     16
##  4: 10.5 -0.017320378     16
##  5: 13.5 -0.027725556     16
##  6: 16.5  0.008925402     16
##  7: 19.5 -0.010678355     16
##  8: 22.5 -0.079405018     24
##  9: 25.5 -0.012031106     16
## 10: 28.5 -0.017813543     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7823  -0.1833   0.0022   0.2021   0.7030  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.08025    0.03159  -2.541   0.0113 *
## timeNorm     0.06361    0.03395   1.874   0.0613 .
## obj.diff     0.06975    0.04864   1.434   0.1520  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06414613)
## 
##     Null deviance: 48.468  on 753  degrees of freedom
## Residual deviance: 48.174  on 751  degrees of freedom
## AIC: 73.823
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n    pval
##  1:      1.5      0.4587912     0.5021701 -0.029565591 52  0.5 :(
##  2:      4.5      0.4157509     0.4581003 -0.036201957 78 0.23 :(
##  3:      7.5      0.4432234     0.4705078 -0.025469870 78 0.34 :(
##  4:     10.5      0.4304029     0.4361551 -0.003198821 78 0.91 :(
##  5:     13.5      0.3406593     0.3993679 -0.061812903 78 0.028 *
##  6:     16.5      0.4468864     0.4316421  0.017600071 78 0.45 :(
##  7:     19.5      0.3992674     0.4386951 -0.038308162 78  0.2 :(
##  8:     22.5      0.4065934     0.3910376  0.012148843 78 0.56 :(
##  9:     25.5      0.3919414     0.3686849  0.027863007 78 0.37 :(
## 10:     28.5      0.3168498     0.3329405 -0.021946631 78 0.56 :(
##     time   error.diff shapes
##  1:  1.5 -0.029565591     16
##  2:  4.5 -0.036201957     16
##  3:  7.5 -0.025469870     16
##  4: 10.5 -0.003198821     16
##  5: 13.5 -0.061812903     24
##  6: 16.5  0.017600071     16
##  7: 19.5 -0.038308162     16
##  8: 22.5  0.012148843     16
##  9: 25.5  0.027863007     16
## 10: 28.5 -0.021946631     16

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.80757  -0.17473   0.04713   0.10223   0.69034  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.116905   0.047140   2.480   0.0137 *  
## timeNorm    -0.003195   0.056222  -0.057   0.9547    
## obj.diff    -0.297981   0.047296  -6.300 1.11e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07124426)
## 
##     Null deviance: 23.276  on 289  degrees of freedom
## Residual deviance: 20.447  on 287  degrees of freedom
## AIC: 61.893
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5428571     0.6390463 -0.11798394 20     0.097 .
##  2:      4.5      0.6666667     0.6686706 -0.02560888 30     0.79 :(
##  3:      7.5      0.5809524     0.7179520 -0.13199277 30   0.0081 **
##  4:     10.5      0.5952381     0.7022945 -0.10466869 30     0.058 .
##  5:     13.5      0.6047619     0.7355270 -0.11701294 30    0.003 **
##  6:     16.5      0.5571429     0.6316433 -0.11730187 30     0.14 :(
##  7:     19.5      0.6000000     0.6735104 -0.09779902 30     0.17 :(
##  8:     22.5      0.6428571     0.7285240 -0.12471305 30     0.012 *
##  9:     25.5      0.4904762     0.6387517 -0.13496951 30 9.2e-06 ***
## 10:     28.5      0.6095238     0.6238117 -0.05083013 30      0.3 :(
##     time  error.diff shapes
##  1:  1.5 -0.11798394     16
##  2:  4.5 -0.02560888     16
##  3:  7.5 -0.13199277     24
##  4: 10.5 -0.10466869     16
##  5: 13.5 -0.11701294     24
##  6: 16.5 -0.11730187     16
##  7: 19.5 -0.09779902     16
##  8: 22.5 -0.12471305     24
##  9: 25.5 -0.13496951     24
## 10: 28.5 -0.05083013     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79242  -0.11474   0.04324   0.10863   0.79907  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.01111    0.02414  -0.460    0.646    
## timeNorm     0.04278    0.03211   1.332    0.183    
## obj.diff    -0.20131    0.02507  -8.031 3.56e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06278399)
## 
##     Null deviance: 53.141  on 782  degrees of freedom
## Residual deviance: 48.972  on 780  degrees of freedom
## AIC: 59.665
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.4576720     0.5856813 -0.13077850 54   0.0017 **
##  2:      4.5      0.5255732     0.6540986 -0.10505796 81 3.3e-07 ***
##  3:      7.5      0.4109347     0.4885319 -0.08564427 81   0.0024 **
##  4:     10.5      0.4726631     0.5978029 -0.10758153 81 2.4e-06 ***
##  5:     13.5      0.4550265     0.5802643 -0.11785181 81 1.9e-06 ***
##  6:     16.5      0.4126984     0.5255582 -0.10656642 81 4.5e-05 ***
##  7:     19.5      0.5044092     0.5760814 -0.07137410 81   0.0011 **
##  8:     22.5      0.4179894     0.5370262 -0.11323691 81   4e-05 ***
##  9:     25.5      0.5291005     0.5877937 -0.08344696 81   2e-04 ***
## 10:     28.5      0.4991182     0.5957763 -0.10218991 81 6.6e-07 ***
##     time  error.diff shapes
##  1:  1.5 -0.13077850     24
##  2:  4.5 -0.10505796     24
##  3:  7.5 -0.08564427     24
##  4: 10.5 -0.10758153     24
##  5: 13.5 -0.11785181     24
##  6: 16.5 -0.10656642     24
##  7: 19.5 -0.07137410     24
##  8: 22.5 -0.11323691     24
##  9: 25.5 -0.08344696     24
## 10: 28.5 -0.10218991     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.68620  -0.11533   0.00271   0.14644   0.85612  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.00914    0.02642  -0.346    0.730    
## timeNorm    -0.03358    0.03694  -0.909    0.364    
## obj.diff    -0.23168    0.02816  -8.228 1.39e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05848987)
## 
##     Null deviance: 36.064  on 550  degrees of freedom
## Residual deviance: 32.052  on 548  degrees of freedom
## AIC: 4.4272
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.3270677     0.5824937 -0.21975183 38 4.8e-08 ***
##  2:      4.5      0.4385965     0.5178656 -0.07591147 57   0.0016 **
##  3:      7.5      0.4010025     0.4897451 -0.07897515 57 0.00024 ***
##  4:     10.5      0.3834586     0.4975966 -0.09894713 57 1.6e-06 ***
##  5:     13.5      0.3032581     0.4554127 -0.14075030 57 1.1e-06 ***
##  6:     16.5      0.3182957     0.4926908 -0.13608760 57 7.2e-08 ***
##  7:     19.5      0.3483709     0.4896047 -0.10281699 57 2.5e-06 ***
##  8:     22.5      0.3383459     0.5206631 -0.17089406 57 1.1e-07 ***
##  9:     25.5      0.4411028     0.5600315 -0.10217279 57 6.4e-05 ***
## 10:     28.5      0.3283208     0.5082965 -0.14696334 57 4.7e-06 ***
##     time  error.diff shapes
##  1:  1.5 -0.21975183     24
##  2:  4.5 -0.07591147     24
##  3:  7.5 -0.07897515     24
##  4: 10.5 -0.09894713     24
##  5: 13.5 -0.14075030     24
##  6: 16.5 -0.13608760     24
##  7: 19.5 -0.10281699     24
##  8: 22.5 -0.17089406     24
##  9: 25.5 -0.10217279     24
## 10: 28.5 -0.14696334     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6762  -0.2381   0.1900   0.2315   0.4465  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.31017    0.08980   3.454 0.000635 ***
## timeNorm     0.04949    0.07512   0.659 0.510564    
## obj.diff    -0.67082    0.08619  -7.783 1.29e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1143324)
## 
##     Null deviance: 40.995  on 289  degrees of freedom
## Residual deviance: 32.813  on 287  degrees of freedom
## AIC: 199.06
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5428571     0.8798433 -0.30689896 20 1.9e-06 ***
##  2:      4.5      0.5809524     0.8853752 -0.26001959 30 5.6e-05 ***
##  3:      7.5      0.6714286     0.8628805 -0.13732681 30 0.00028 ***
##  4:     10.5      0.6095238     0.7642684 -0.13465409 30     0.026 *
##  5:     13.5      0.6238095     0.7611007 -0.13731106 30     0.12 :(
##  6:     16.5      0.6142857     0.7965648 -0.14899512 30   0.0011 **
##  7:     19.5      0.5619048     0.7241382 -0.10999921 30      0.1 :(
##  8:     22.5      0.4809524     0.7120972 -0.18492560 30 0.00042 ***
##  9:     25.5      0.5666667     0.6598092 -0.08410354 30     0.38 :(
## 10:     28.5      0.6238095     0.6649412 -0.04563626 30     0.37 :(
##     time  error.diff shapes
##  1:  1.5 -0.30689896     24
##  2:  4.5 -0.26001959     24
##  3:  7.5 -0.13732681     24
##  4: 10.5 -0.13465409     24
##  5: 13.5 -0.13731106     16
##  6: 16.5 -0.14899512     24
##  7: 19.5 -0.10999921     16
##  8: 22.5 -0.18492560     24
##  9: 25.5 -0.08410354     16
## 10: 28.5 -0.04563626     16
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6900  -0.3151   0.0800   0.2502   0.5349  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.31221    0.04534   6.885 1.58e-11 ***
## timeNorm    -0.02431    0.05181  -0.469    0.639    
## obj.diff    -0.61092    0.04689 -13.028  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1054177)
## 
##     Null deviance: 76.912  on 550  degrees of freedom
## Residual deviance: 57.769  on 548  degrees of freedom
## AIC: 329.01
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.4398496     0.6300933 -0.177313643 38    0.004 **
##  2:      4.5      0.5764411     0.7678535 -0.202298748 57 1.6e-05 ***
##  3:      7.5      0.5764411     0.6011087 -0.043571927 57     0.33 :(
##  4:     10.5      0.5989975     0.5592427  0.022877420 57     0.67 :(
##  5:     13.5      0.5162907     0.5862626 -0.072128673 57     0.16 :(
##  6:     16.5      0.6165414     0.5965834  0.010731991 57     0.86 :(
##  7:     19.5      0.4561404     0.5365408 -0.091070671 57     0.11 :(
##  8:     22.5      0.5162907     0.4836344  0.029379577 57     0.52 :(
##  9:     25.5      0.4461153     0.4556433 -0.006625731 57     0.93 :(
## 10:     28.5      0.3934837     0.3833288 -0.006660789 57     0.89 :(
##     time   error.diff shapes
##  1:  1.5 -0.177313643     24
##  2:  4.5 -0.202298748     24
##  3:  7.5 -0.043571927     16
##  4: 10.5  0.022877420     16
##  5: 13.5 -0.072128673     16
##  6: 16.5  0.010731991     16
##  7: 19.5 -0.091070671     16
##  8: 22.5  0.029379577     16
##  9: 25.5 -0.006625731     16
## 10: 28.5 -0.006660789     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6893  -0.1799  -0.1005   0.2162   0.7143  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.08745    0.02950   2.964  0.00312 ** 
## timeNorm     0.06180    0.03854   1.604  0.10917    
## obj.diff    -0.34673    0.03733  -9.289  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.08348187)
## 
##     Null deviance: 76.797  on 811  degrees of freedom
## Residual deviance: 67.537  on 809  degrees of freedom
## AIC: 293.05
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.3469388     0.4886803 -0.125553103 56 0.00094 ***
##  2:      4.5      0.4268707     0.4741611 -0.054867444 84     0.18 :(
##  3:      7.5      0.3945578     0.4308157 -0.046659883 84     0.25 :(
##  4:     10.5      0.3877551     0.4327296 -0.047305474 84     0.084 .
##  5:     13.5      0.3962585     0.3887083  0.010385088 84     0.77 :(
##  6:     16.5      0.3435374     0.3279099  0.001173126 84     0.98 :(
##  7:     19.5      0.3418367     0.2728660  0.039134970 84     0.31 :(
##  8:     22.5      0.2755102     0.2463567  0.021040070 84     0.63 :(
##  9:     25.5      0.2789116     0.2412372  0.017366417 84     0.65 :(
## 10:     28.5      0.2823129     0.2192474  0.018899358 84     0.73 :(
##     time   error.diff shapes
##  1:  1.5 -0.125553103     24
##  2:  4.5 -0.054867444     16
##  3:  7.5 -0.046659883     16
##  4: 10.5 -0.047305474     16
##  5: 13.5  0.010385088     16
##  6: 16.5  0.001173126     16
##  7: 19.5  0.039134970     16
##  8: 22.5  0.021040070     16
##  9: 25.5  0.017366417     16
## 10: 28.5  0.018899358     16